SAMPLER: Empirical distribution representations for rapid analysis of whole slide tissue images.

ABSTRACT: Deep learning has revolutionized digital pathology, allowing for automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. In such analyses, WSIs are typically broken into smaller images called tiles, and a neural network backbone encodes each tile in a feature space. Many recent works have applied attention based deep learning models to aggregate tile-level features into a slide-level representation, which is then used for slide-level prediction tasks. However, training attention models is computationally intensive, necessitating hyperparameter optimization and specialized training procedures. Here, we propose SAMPLER, a fully statistical approach to generate efficient and informative WSI representations by encoding the empirical cumulative dis

SUBMITTER: Mukashyaka P 

PROVIDER: S-EPMC10418159 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature